• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.11a
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• pp.656-658
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• 2006

This study deals with the part-machine grouping (PMG) that considers realistic manufacturing factors, such as the machine duplication, operation sequences with multiple visits to the same machine, and production volumes of parts. Basically, this study is an extension of Won(2006) that has adopted fuzzy ART neural network to group parts and machines. The proposed fuzzy ART neural network algorithm is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Computational experiments applied to large-size PMG data sets with a psuedo-replicated clustering procedure show effectiveness of the proposed approach.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.35-39
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• 2005

We apply estimations of the intuitionistic fuzzy sets on the basis of which some amendments may be undertaken.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.3-6
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• 2006

Methods for risk engineering is a bundle of engineering tools including fundamental concepts and approaches of soft computing with application to real issues of risk management. In this talk fundamental concepts and soft computing approaches of risk engineering will be introduced. As the term of risk implies both advantageous and hazardous uncertainty in its origins, a fundamental theory to describe uncertainties is introduced that includes traditional probability and statistical models, fuzzy systems, as well as less popular modal logic. In particular, modal logic capabilities to express various kinds of uncertainties are emphasized and relations with rough sets and evidence theory are described. Another topic is data mining related to problems in risk management. Some risk mining techniques including fuzzy clustering are introduced and a recently developed algorithm is overviewed. A numerical example is shown.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.369-371
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• 2009

휴머노이드 로봇에서 스스로 환경을 인식하는 기술은 필수적으로 필요하다. 그러나 정확하게 환경을 인식하기 위해서는 휴머노이드 움직임을 추정하여 보정해야 한다. 따라서 본 논문에서는 type-2 퍼지셋을 이용하여 휴머노이드 로봇의 움직임을 스테레오 비전기반으로 추정하는 방법을 제안하고자 한다. 본 연구에서는 우선 스테레오 비전으로 얻은 Disparity Map을 Type-2 퍼지셋을 이용하여 추적할 대상을 추출한다. 추출된 대상은 보다 정확한 계산을 위하여 Wavelet Transform을 이용하여 정보량을 확장하였으며, 그 결과로 얻어진 결과영상들은 다시 Least suqare approximation과 Type-2 퍼지셋을 이용하여 하나의 결과값으로 나타내어진다. 연속된 두 이미지의 움직임을 추정함으로써 로봇의 움직임을 추정하게 된다. 제안된 방법은 실험을 통하여 그 추정 정확도나 연산속도 면에서 효율적임을 알 수 있었다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.131-134
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• 1996

Application of machine learning to scheduling problems has focused on improving system performance based on opportunistic selection among multitudes of simple rules. This study proposes a new method of learning scheduling rules, which first establishes qualitatively meaningful criteria and quantitatively optimizes the use of them, a similar way as human scheduler accumulate their expertise. The weighting of these criteria is trained in response to the system states through simulation. To mimic human quantitative feelings, distributed fuzzy sets are used for assessing the system state. The proposed method was applied to job dispatching in a simulated FMS environment. The job-dispatching criteria used were the length of the processing time of a job and the situation of the next workstation. The results show that the proposed method can develop efficient and robust scheduling strategies, which can also provide understandable and usable know-hows to the human scheduler.

• Computers and Concrete
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• v.26 no.5
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• pp.397-409
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• 2020

The current study targets to estimate the contribution of the end-anchored FRP composites in resisting shear force using a soft computing tool i.e., adaptive neuro-fuzzy inference system (ANFIS). A total of 107 sets of data accumulated from literature was utilized for the development and evaluation of the current ANFIS model. A comparative analysis between the ANFIS predictions and the acquired experimental results has shown that the ANFIS predictions are in very good agreement with that of experimental ones. Additionally, the accuracy of the current ANFIS model has been weighed up against the estimates of nine widely adopted design guidelines. Based on various statistical parameters, it has been deduced that the effectiveness of the current ANFIS model is better than the considered design guidelines. Besides this, a parametric study was carried out to explore the combined effect of different parameters as well as the impact of individual parameters.

• Proceedings of the Korean Society for Quality Management Conference
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• 2004.04a
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• pp.48-53
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• 2004

As many organizations are searching for ways to compete more effectively in today's market environment. Image of Product is become the most important fact to improve their competition. The objectives of this paper are to provide an overview of PDM(Purchasing Decision Factor) and to discuss how to measure it more efficiently. This study develops a conceptual 'relation model of the purchasing decision factor', which identifies only performance based measurement, and proposes Fuzzy Measuring Method which uses the Fuzzy rule based algorithm to adept survey to date sets.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.961-968
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• 2018

Recently, researches on automatic recognition of human activities have been actively carried out with the emergence of various intelligent systems. Since a large amount of visual data can be secured through Closed Circuit Television, it is required to recognize human behavior in a dynamic situation rather than a static situation. In this paper, we propose new intelligent human activity recognition model using the trajectory information extracted from the video sequence. The proposed model consists of three steps: segmentation and partitioning of trajectory step, feature extraction step, and behavioral learning step. First, the entire trajectory is fuzzy partitioned according to the motion characteristics, and then temporal features and spatial features are extracted. Using the extracted features, four pedestrian behaviors were modeled by decision tree learning algorithm and performance evaluation was performed. The experiments in this paper were conducted using Caviar data sets. Experimental results show that trajectory provides good activity recognition accuracy by extracting instantaneous property and distinctive regional property.

• Honam Mathematical Journal
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• v.28 no.3
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• pp.417-431
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• 2006

The notions of IVF strongly semiopen (semiclosed) mappings and IVF strongly semicontinuous mappings are introduced, and several examples are provided. Related properties are investigated. In particular, characterizations of an IVF strongly semiopen mapping and an IVF strongly semicontinuous mapping are discussed.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.396-398
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• 2003

This paper presents the scheme for load model based dispersed generation system (DGs) installation and operation in unbalanced distribution systems. Groups of each individual load model consist of residential, industrial, commercial, official and agricultural load. The main idea of solving fuzzy nonlinear goal programming is to transform the original objective function and constraints into the equivalent multiple objective functions with fuzzy sets to evaluate their imprecise nature for the criterion of power loss minimization, the number or total capacity of DGs and the bus voltage deviation, and then solve the problem using genetic algorithms. The method proposed was applied to IEEE 13 bus test systems to demonstrate its effectiveness.